IMPORTENT! READ THIS!

BEST USE:

The dataset was first tokenized with Spacy for better results of the model, so even though you can use the model with the pipeline API (described in Direct Use) it is highly recommended to use this way:

from transformers import AutoTokenizer, AutoModelForTokenClassification
import spacy

nlp = spacy.load("en_core_web_sm")
tokenizer = AutoTokenizer.from_pretrained("JonyC/scibert-NER-finetuned-improved")
model = AutoModelForTokenClassification.from_pretrained("JonyC/scibert-NER-finetuned-improved")
id2label = model.config.id2label

def predict_scibert_labels(sentence):
    # Step 1: SpaCy tokenization
    words = [token.text for token in nlp(sentence) if not token.is_space]
    # alternative remove entities:
    # words = [token.text for token in nlp(sentence) if not token.is_space and not token.ent_type_]
    # Step 2: Tokenize with SciBERT using the words
    inputs = tokenizer(
        words,
        is_split_into_words=True,
        return_tensors="pt",
        truncation=True,
        padding=True
    )

    with torch.no_grad():
        outputs = model(**inputs).logits  # (1, seq_len, num_labels)
    
    predictions = torch.argmax(outputs, dim=-1).squeeze().tolist()
    word_ids = inputs.word_ids()

    # Step 3: Align predictions to original words (skip subwords)
    final_tokens = []
    final_labels = []

    previous_word_idx = None
    for i, word_idx in enumerate(word_ids):
        if word_idx is None or word_idx == previous_word_idx:
            continue
        label = id2label[predictions[i]]
        final_tokens.append(words[word_idx])
        final_labels.append(label)
        previous_word_idx = word_idx

    return list(zip(final_tokens, final_labels))

output:

CRISPR          -> B-Scns
-               -> I-Scns
Cas9            -> I-Scns
is              -> O
a               -> O
powerful        -> O
tool            -> O
for             -> O
genome          -> B-Scns
editing         -> O
.               -> O

Model Card for Model ID

This model is a fine-tuned version of allenai/scibert_scivocab_uncased for scientific terms/phrases detection in text. It is trained on a custom dataset JonyC/ScienceGlossary-NER_fit for Named Entity Recognition (NER), aiming to identify scientific terms in a variety of academic and technical texts.

Model Details

Model Description

This model has been fine-tuned for the task of scientific term detection. It classifies words as scientific terms, using the Scns label to denote scientific terms and the O label for non-scientific terms. The model has been trained on a custom dataset, which makes it effective for extracting scientific terms from academic and technical texts.

Developed by: [JonyC]
Model type: BERT-based token classifier
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model: allenai/scibert_scivocab_uncased

Uses

Direct Use

This model can be used directly to detect scientific terms in text. You can apply it to text data where you want to extract scientific terminology with the huggingface pipline like this:

from transformers import pipeline

# Use a model from Hugging Face Hub directly in the notebook
pipe = pipeline("token-classification", model="JonyC/scibert-NER-finetuned-improved")
sentence = "CRISPR-Cas9 is a powerful tool for genome editing."

result = pipe(sentence)
result

results:

[{'entity': 'B-Scns', 'score': 0.9897461, 'index': 1, 'word': 'crispr', 'start': 0, 'end': 6},
 {'entity': 'I-Scns', 'score': 0.9474513, 'index': 2, 'word': '-', 'start': 6, 'end': 7},
 {'entity': 'I-Scns', 'score': 0.97595257, 'index': 3, 'word': 'cas', 'start': 7, 'end': 10},
 {'entity': 'I-Scns', 'score': 0.9894609, 'index': 4, 'word': '##9', 'start': 10, 'end': 11},
 {'entity': 'B-Scns', 'score': 0.999246, 'index': 10, 'word': 'genome', 'start': 35, 'end': 41}]

Out-of-Scope Use

This model is not intended for general-purpose NER tasks outside the scope of scientific term detection. It may perform poorly on tasks unrelated to scientific term extraction, for example entities recognition.

Bias, Risks, and Limitations

While this model is designed to identify scientific terms, there may be limitations in recognizing terms from specialized subfields of science that were not represented in the training data. Additionally, the model might struggle with terms that are ambiguous or have multiple meanings depending on the context. The model mainly trained on less familiar terms so it might recognise the most special term and less the superfical terms (e.g. it doesnt label computer as scientific). and it very bais towards people name and places, so you might want to combine the use of ner in Spacy.

Recommendations

Users should be cautious when applying the model to texts that differ significantly from the training data. The model is optimized for extracting scientific terms but may not perform well for general NER tasks. Further fine-tuning might be necessary for specific domains or types of terminology. best use with Spacy to remove entities.

Training Details

Training Data

The model was fine-tuned on the JonyC/ScienceGlossary-NER_fit dataset, which consists of scientific texts annotated with scientific terms.

Training Procedure The model was trained using the following parameters:

Epochs: 10

Learning Rate: 3e-6

Batch Size: 16

Weight Decay: 0.05

Scheduler: Cosine learning rate decay

Preprocessing

It's all written in JonyC/ScienceGlossary-NER_fit

Training Hyperparameters

Training regime: fp16 mixed precision

Optimizer: AdamW

Loss function: Cross-entropy loss

Evaluation

The The evaluation on the validation set are: Validation set:

Precision: 0.9283
Recall: 0.941
F1: 0.9346
Accuracy: 0.9833
Loss: 0.05465

The model also was evaluated on a held-out test set. Test set:

Precision: 0.9278
Recall: 0.9403
F1: 0.9341
Accuracy: 0.9834
Loss: 0.05307

Testing Data, Factors & Metrics

Testing Data

JonyC/ScienceGlossary-NER_fit

Metrics

The evaluation metrics include precision, recall, F1 score, and accuracy, which are standard for token classification tasks. These metrics provide a comprehensive understanding of the model's ability to identify scientific terms.

metric = evaluate.load("seqeval")

Results

On the test set, the model achieved an accuracy of 98.34% with a F1 score of 0.934, demonstrating its effectiveness at detecting scientific terms.

Citation

BibTeX: @misc{scibert-NER-finetuned-improved, author = {JonyC}, title = {SciBERT for Scientific Term Detection}, year = {2025}, url = {https://huggingface.co/JonyC/scibert-NER-finetuned-improved} } APA: JonyC. (2025). SciBERT for Scientific Term Detection. Hugging Face. https://huggingface.co/JonyC/scibert-NER-finetuned-improved

Author: JonyC

Model Card Contact

For questions, contributions, or collaborations, feel free to contact me: 📧 [email protected]

JonyC
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scibert-NER-finetuned-improved